Catanova for multidimensional contingency tables: Nominal-scale response

This paper extends an analysis of variance for categorical data (CATANOVA) procedure to multidimensional contingency tables involving several factors and a response variable measured on a nominal scale. Using an appropriate measure of total variation for multinomial data, partial and multiple association measures are developed as R² quantities which parallel the analogous statistics in multiple linear regression for quantitative data. In addition, test statistics are derived in terms of these R² criteria. Finally, this CATANOVA approach is illustrated within the context of 2 three-way contingency table from a multicenter clinicaltrial.     

中国人身保险需求的实证分析

从理论角度分析了GDP、收入水平、人口因素、社会保障、通货膨胀等因素对我国人身保险需求的影响,并依1990年到2005年的相关数据,运用因子分析、平稳性检验、多元线性回归等方法对上述因素进行了实证分析.在分析中针对多重共线性问题,引入偏最小二乘回归对模型加以改进.同时,对我国人身保险市场发展提出了要加大寿险宣传力度、创造良好宽松的外部环境、优化险种结构、完善保险法规建设等建议.     

Stone—Geisser检验在顾客满意度中的应用

偏最小二乘法（PLS）模型对数据没有做任何的分布假设，因此基于分布假设的统计检验方法无法实现对PLS模型预测效度的度量。采用非参数检验方法——Stone-Geisser检验，通过盲目（Blindfold）过程进行预测效度的度量。使用MATLAB软件实现了两种Stone-Geisser检验——公因子方差检验与冗余检验。在地税部门顾客满意度应用中，Stone-Geisser检验结果与PLS模型其他结果保持内在一致，从而表明检验方法是有效的。因为Stone-Geisser检验基于交叉验证技术，因此非常适合作为PLS模型的基准检验方法。     

On linear combinations of independent exponential variables

The exact distribution of a linear combination of n indepedent negative exponential random variables , when the coefficients cf the linear combination are distinct and positive , is well-known. Recently Ali and Obaidullah (1982) extended this result by taking the coeff icients to be arbitrary real numbers. They used a lengthy geometric.

al approach to arrive at the result . This article gives a simple derivation of the result with the help of a generalized partial fraction technique. This technique also works when the variables involved are gamma variables with certain types of parameters. Results are presented in a form which can easily be programmed for computational purposes. Connection of this problem t o various problems in different fields is also pointed out.     

马克思的生产率理论再考察

西方国家的劳动生产率概念不同于马克思的劳动生产率概念，马克思已明确区分劳动生产率和劳动过程的各种投入要素的使用效率，即西方经济学中使用partialproductivity，马克思的劳动生产率的内涵是指总劳动过程的最终效率。马克思的劳动生产率概念类似当代西方经济学使用的TotalFactorProductivity，但两者又有区别。     

Pivotal ouantities based on the conditional distribution function transform in models with accessory parameters

A general formula for porducing pivotal quantities defined free of accessory paramelers is investigated. In addition to similarity, resulting tests often have exact or asymptotic optimal properties In other cases, the sample correlation with statistics sufficient for the accescory parameters produce a simple similar test and a consistent, asymptctically normal estimator. The latter mathod should perform best when the accessories are highly dispersed.     

Sensory analysis via multi-block multivariate additive PLS splines

In the last decade, much effort has been spent on modelling dependence between sensory variables and chemical–physical ones, especially when observed at different occasions/spaces/times or if collected from several groups (blocks) of variables. In this paper, we propose a nonlinear generalization of multi-block partial least squares with the inclusion of variable interactions. We show the performance of the method on a known data set.     

Empirical Likelihood for a Heteroscedastic Partial Linear Errors-in-Variables Model

The purpose of this article is to use the empirical likelihood method to study construction of the confidence region for the parameter of interest in heteroscedastic partially linear errors-in-variables model with martingale difference errors. When the variance functions of the errors are known or unknown, we propose the empirical log-likelihood ratio statistics for the parameter of interest. For each case, a nonparametric version of Wilks’ theorem is derived. The results are then used to construct confidence regions of the parameter. A simulation study is carried out to assess the performance of the empirical likelihood method.     

Flexible Servers in Understaffed Tandem Lines

We study the dynamic assignment of cross‐trained servers to stations in understaffed lines with finite buffers. Our objective is to maximize the production rate. We identify optimal server assignment policies for systems with three stations, two servers, different flexibility structures, and either deterministic service times and arbitrary buffers or exponential service times and small buffers. We use these policies to develop server assignment heuristics for Markovian systems with larger buffer sizes that appear to yield near‐optimal throughput. In the deterministic setting, we prove that the best possible production rate with full server flexibility and infinite buffers can be attained with partial flexibility and zero buffers, and we identify the critical skills required to achieve this goal. We then present numerical results showing that these critical skills, employed with an effective server assignment policy, also yield near‐optimal throughput in the Markovian setting, even for small buffer sizes. Thus, our results suggest that partial flexibility is sufficient for near‐optimal performance, and that flexibility structures that are effective for deterministic and infinite‐buffered systems are also likely to perform well for finite‐buffered stochastic systems.     

Lasso penalized semiparametric regression on high-dimensional recurrent event data via coordinate descent

This paper studies a fast computational algorithm for variable selection on high-dimensional recurrent event data. Based on the lasso penalized partial likelihood function for the response process of recurrent event data, a coordinate descent algorithm is used to accelerate the estimation of regression coefficients. This algorithm is capable of selecting important predictors for underdetermined problems where the number of predictors far exceeds the number of cases. The selection strength is controlled by a tuning constant that is determined by a generalized cross-validation method. Our numerical experiments on simulated and real data demonstrate the good performance of penalized regression in model building for recurrent event data in high-dimensional settings.